Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
Bibliographical noteFunding Information:
This study was supported by the National Institute of Mental Health, MH-077851, MH-078113, MH-077945, MH-077852, and MH-077862. GP has served on an advisory panel for Bristol-Meyer Squibb; CT serves on an advisory board for Karuna and KyNexis, and an advisory panel for Sunovion, Astellas, and Merck; MSK has received research support from Sunovion and GlaxoSmithKline. The remaining authors declare no competing interests.
PubMed: MeSH publication types
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